ShiftCode Analytics
Visa: No H1b,OPT,CPT rest any visa
Location: Worksite: Hybrid Onsite mandator Mon-Thur - Houston, TX
Locals only, PhD or aspiring PhD is highly preferred,Palantir experience will secure an interview
Interview: 1st round video and second 50% possiblity of F2F interview
Key Responsibilities: AI/ML Model Development:
Design, train, and fine-tune machine learning and deep learning models, including
LLMs , for
predictive analytics, automation, and AI-driven decision-making . Data Analysis & Feature Engineering:
Collect, process, and analyze
structured and unstructured data , engineering relevant features to improve model performance. Agent-Based & NLP Applications:
Develop
LLM-based AI solutions
with a focus on
prompt engineering, fine-tuning, and inference optimization . Business Impact & Decision Support:
Translate complex data science methodologies into actionable insights, collaborating with stakeholders to drive business value. End-to-End Model Deployment:
Work with
MLOps best practices
to deploy and monitor models in production, ensuring
scalability, efficiency, and reliability . Data Storytelling & Visualization:
Develop
clear, compelling presentations
and dashboards to communicate findings to non-technical stakeholders.
Requirements: Technical Skills:
AI & Machine Learning:
Experience in
predictive modeling, NLP, deep learning, and LLM-based applications
(e.g., GPT, BERT, LangChain). Programming:
Proficiency in
Python
and experience with AI/ML frameworks (e.g.,
PyTorch, TensorFlow, Hugging Face ). Data Engineering & SQL:
Ability to write efficient
SQL queries
to blend and structure data from multiple sources for modeling and analysis. Cloud & MLOps:
Experience with
AWS (SageMaker, S3, Redshift), Snowflake, and ML pipeline automation . Version Control & Collaboration:
Proficiency using
Git
for code versioning and teamwork. Soft Skills:
Curious & Innovative:
Passionate about solving complex business problems using data and AI. Ownership & Initiative:
Proactively drive projects from conception to deployment. Business Acumen:
Understand
how AI/ML solutions impact business goals and decision-making . Effective Communication:
Ability to explain
technical models and AI methodologies
to non-technical audiences.
Preferred Qualifications:
Graduate degree (Master's or Ph.D.)
in a
quantitative field
(e.g.,
Computer Science, Data Science, Statistics, Engineering, Mathematics, Economics ). Experience with dashboarding tools
(e.g.,
Power BI, Dash, Streamlit ) for model performance monitoring. Familiarity with reinforcement learning and AI agent-based applications
.
This role is ideal for a
Data Scientist
who wants to work at the
cutting edge of AI and ML , leveraging
LLMs, NLP, and predictive analytics
to drive meaningful impact.
Key Responsibilities: AI/ML Model Development:
Design, train, and fine-tune machine learning and deep learning models, including
LLMs , for
predictive analytics, automation, and AI-driven decision-making . Data Analysis & Feature Engineering:
Collect, process, and analyze
structured and unstructured data , engineering relevant features to improve model performance. Agent-Based & NLP Applications:
Develop
LLM-based AI solutions
with a focus on
prompt engineering, fine-tuning, and inference optimization . Business Impact & Decision Support:
Translate complex data science methodologies into actionable insights, collaborating with stakeholders to drive business value. End-to-End Model Deployment:
Work with
MLOps best practices
to deploy and monitor models in production, ensuring
scalability, efficiency, and reliability . Data Storytelling & Visualization:
Develop
clear, compelling presentations
and dashboards to communicate findings to non-technical stakeholders.
Requirements: Technical Skills:
AI & Machine Learning:
Experience in
predictive modeling, NLP, deep learning, and LLM-based applications
(e.g., GPT, BERT, LangChain). Programming:
Proficiency in
Python
and experience with AI/ML frameworks (e.g.,
PyTorch, TensorFlow, Hugging Face ). Data Engineering & SQL:
Ability to write efficient
SQL queries
to blend and structure data from multiple sources for modeling and analysis. Cloud & MLOps:
Experience with
AWS (SageMaker, S3, Redshift), Snowflake, and ML pipeline automation . Version Control & Collaboration:
Proficiency using
Git
for code versioning and teamwork. Soft Skills:
Curious & Innovative:
Passionate about solving complex business problems using data and AI. Ownership & Initiative:
Proactively drive projects from conception to deployment. Business Acumen:
Understand
how AI/ML solutions impact business goals and decision-making . Effective Communication:
Ability to explain
technical models and AI methodologies
to non-technical audiences.
Preferred Qualifications:
Graduate degree (Master's or Ph.D.)
in a
quantitative field
(e.g.,
Computer Science, Data Science, Statistics, Engineering, Mathematics, Economics ). Experience with dashboarding tools
(e.g.,
Power BI, Dash, Streamlit ) for model performance monitoring. Familiarity with reinforcement learning and AI agent-based applications
.
This role is ideal for a
Data Scientist
who wants to work at the
cutting edge of AI and ML , leveraging
LLMs, NLP, and predictive analytics
to drive meaningful impact.